Future prediction of Siberian wildfire and aerosol emissions via the improved fire module of the spatially explicit individual-based dynamic global vegetation model

Nurrohman, Reza Kusuma; Kato, Tomomichi; Ninomiya, Hideki; Végh, Lea; Delbart, Nicolas; Miyauchi, Tatsuya; Sato, Hisashi; Shiraishi, Tomohiro; Hirata, Ryuichi

doi:https://doi.org/10.5194/egusphere-2024-105

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https://doi.org/10.5194/egusphere-2024-105

Preprints

16 Jan 2024

| 16 Jan 2024

Future prediction of Siberian wildfire and aerosol emissions via the improved fire module of the spatially explicit individual-based dynamic global vegetation model

Reza Kusuma Nurrohman, Tomomichi Kato, Hideki Ninomiya, Lea Végh, Nicolas Delbart, Tatsuya Miyauchi, Hisashi Sato, Tomohiro Shiraishi, and Ryuichi Hirata

Abstract. Fires are among the most influential disturbances affecting ecosystem structure and biogeochemical cycles in Siberia. Therefore, precise fire modeling via dynamic global vegetation models is important for predicting greenhouse gas emissions and other burning biomass emissions to understand changes in biogeochemical cycles. In this study, we integrated the widely used SPread and InTensity of FIRE (SPITFIRE) fire module into the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM) to improve the accuracy of fire predictions and then simulated future fire regimes to better understand their impacts. Under the Representative Concentration Pathways 8.5 climate scenario, we estimated that the CO₂, CO, PM_2.5, total particulate matter (TPM), and total particulate carbon (TPC) emissions in Siberia will continue to increase annually until 2100 by an average of 214.4, 17.16, 2.8, 2.1, and 1.47 Gg species year^-1, respectively. Under the same scenario and period, 185 trees ha^-1 year^-1 are estimated to be killed by wildfires, resulting in a 319.3 g C m^-2 year^-1 loss of net primary production (NPP). These findings show that Siberia faces an increasing frequency of extreme fire events due to changing climate conditions. Our study offers insights into future fire regimes and provides helpful information for development strategies for enhancing regional resilience and for mitigating the broader environmental consequences of heightened fire activity in Siberia.

Received: 13 Jan 2024 – Discussion started: 16 Jan 2024

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Journal article(s) based on this preprint

26 Sep 2024

Future projections of Siberian wildfire and aerosol emissions

Reza Kusuma Nurrohman, Tomomichi Kato, Hideki Ninomiya, Lea Végh, Nicolas Delbart, Tatsuya Miyauchi, Hisashi Sato, Tomohiro Shiraishi, and Ryuichi Hirata

Biogeosciences, 21, 4195–4227, https://doi.org/10.5194/bg-21-4195-2024,https://doi.org/10.5194/bg-21-4195-2024, 2024

Short summary

Reza Kusuma Nurrohman, Tomomichi Kato, Hideki Ninomiya, Lea Végh, Nicolas Delbart, Tatsuya Miyauchi, Hisashi Sato, Tomohiro Shiraishi, and Ryuichi Hirata

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2024-105', Anonymous Referee #1, 16 Feb 2024
Reza et al. improved the fire module in the SEIB-DGVM model and used the model to project Siberia wildfire dynamics, and emissions under various climate scenarios. I appreciate the valuable model development and analysis, and it provide us important implications on how future climate change affects Siberia boreal forest. The paper is informative and has a lot of important results to discuss, but it is not well-written in terms of smoothness and readability. There are confusions on methodology and data usage. Most importantly, this manuscript over-emphasizes future RCP projections. However, historical validation is not clear and convincing. A more reasonable approach is to first thoroughly demonstrate the model performance during the historical period by comparing it with observations. Then discuss future projections and caveats. Below are my major comments.
There is no quantitative evidence of the model's performance. During the historical period, how did the model simulate burned area, and fire emissions, compared with existing datasets (e.g., GFED) at gridcell level? Plot scatter plots of modeled versus observed variables (burned area, emissions, biomass) will be super helpful. Showing R2 and RMSE of gridcell level comparison in the abstract is highly encouraged. As it is stated in the abstract the motivation for integrating SPITFIRE into DGVM is to improve the accuracy of fire modeling. Therefore, it is also important to show how much improvement has been achieved by SPITFIRE compared with DGVM’s default fire model.

Improvement from annual time step estimate to monthly is an important change. However, most of the fires last less than one month. How does the monthly time step fire module resolve a process that lasts much shorter than a month?

Human ignition is considered in E term (equation 2), how about human suppression on fire spread?

Methodology, between 2.2 model description and 2.3 model application, there seems missing a section about model calibration. How was the SPITFIRE module calibrated for burned areas or emissions? How was the DGVM model calibrated to capture observed AGB?

For future projection, are population density changes and lightning flash changes considered under future scenarios?

Section 2.4, the model is validated by GFED4 burned area and GFED4s burned fraction. However, GFED4s and GFED4 are different because GFED4S include smaller fires. How to reconcile GFED4s and GFED4 during validation?

Section 3.1-3.4. Historical validation is an important component of this study. I would like to suggest before discussing RCP results thoroughly compare the simulated burned area, emission, and biomass with observations. Discuss model performance and biases at gridcell level, and the regional level.

Section 3.5 Fire-off simulation is not mentioned in the methodology section. When was the fire turned off? Does the model run an extended spinup with fire-off? One would expect much larger aboveground biomass when the fire is turned off.

Figure 7 showing spatial coverage is confusing because GFED4s provide burned fraction, which should be directly compared with SEIB-DGVM at gridcell level. Figure 8, a and b are duplicated, both showed long-term monthly average dry matter emission. Figure 9, why dry matter emission is perfectly simulated by SEIB-DGVM (shown in Figure 8), but the performance of CO2 emission is much worse. It seems inconsistent. What does it look like, if plot a spatial average comparison of DM emissions between SEIB-DGVM with GFED4s?
Citation: https://doi.org/10.5194/egusphere-2024-105-RC1
- AC1: 'Reply on RC1', Tomomichi Kato, 06 May 2024
  
  Thank you for your valuable feedback on our manuscript. We appreciate your understanding of importance in Siberia's boreal forests under future climate scenarios. We understand your concerns regarding the clarity and readability of the manuscript, as well as the need for a stronger emphasis on historical validation. We will revise the manuscript to improve its smoothness and address any confusions regarding methodology and data usage. Additionally, we will prioritize demonstrating the model's performance during the historical period before discussing future projections. Once again, thank you for your constructive comments and we have replied to your comments and also adjusted our manuscript accordingly.
  
  Citation: https://doi.org/10.5194/egusphere-2024-105-AC1
RC2:
'Comment on egusphere-2024-105', Anonymous Referee #2, 16 Feb 2024

Please find my review attached.

Citation: https://doi.org/10.5194/egusphere-2024-105-RC2
- AC2: 'Reply on RC2', Tomomichi Kato, 06 May 2024
  
  Thank you for your thorough review of our manuscript, and we appreciate your constructive feedback and insightful suggestions for improvement.
  
  We understand your suggestion regarding the restructuring of the manuscript to enhance its clarity and focus on key aspects. We will carefully reassess the content of the results section to ensure that it effectively communicates the significance of our findings and their implications. Additionally, we acknowledge the importance of prioritizing the evaluation of the model's performance during the historical period before discussing future projections. We will revise the manuscript accordingly to improve its coherence and readability.
  Furthermore, we appreciate your recommendation to include an overview figure or table that systematically compares and summarizes the model performance for the old and new versions. This will indeed provide a clearer demonstration of the improvements made in the new version of the model. Regarding the outdated references, we will thoroughly review the literature and update the references with more recent ones to ensure the accuracy and relevance of our citations.
  
  We are grateful for your suggestion consideration of our manuscript for publication in Biogeosciences after major revision. Your feedback will undoubtedly contribute to enhancing the quality of our work, and we are committed to addressing all your suggestions in our revised manuscript.
  
  Once again, thank you very much for your time and valuable input to our manuscript.
  
  Citation: https://doi.org/10.5194/egusphere-2024-105-AC2
RC3:
'Comment on egusphere-2024-105', Anonymous Referee #3, 17 Feb 2024

This study projected future wildfire activities in Siberia by implementing a process-based fire module into a dynamic vegetation model. The main conclusion was that fire emissions continued to increase due to climate change. The authors also quantified the negative impacts of fire activity on tree mortality and NPP. While the topic of the study well fit the scope of BG, I found the presentation was very poor and many results were contradicting. I suggested a major revision but with strong doubts about the credibility of the model and the projections.
First, the model descriptions were unclear. The authors put most of details, including equations and descriptions, to the supplementary material. However, the key processes should be listed in the main text for clarity. Most important, the links between the fire module and vegetation model should be explicitly explained. For example, what parameters did the SEIB-DGVM provide for SPITFIRE, and how the fire activity affect the vegetation distribution and ecosystem productivity. Such information determines which variables to be validated against observations.
Second, the model validations were questionable. Normally, the validations come before the projections. However, this study put the validations of fire models in the section 3.7, far behind the future projections. Some of the validations were not necessary. For example, Figure S2 compared the input and output of lightning flash rate and population density, which were actually the input of fire models instead of the prediction. Some validations were not consistent between different presentations. For example, Figure S17a compared the simulated and observed dry matter. The model predictions showed poor performance by missing almost all the observed fire episodes. However, in Figure 8, the monthly validations of simulated dry matter showed perfect performance with almost the same magnitude (Figure 8a) and R2=1 (Figure 8b) against observations. These results were too good to be true and seemed not consistent with Figure S17. Furthermore, the perfect simulation of dry matter (Figure 8b) resulted in a poor prediction of CO2 emissions (Figure 9). Was such bias attributed only to the emission factors? Then why making such a great effort to develop the sophisticated fire module when the simplest parameters caused the largest biases? BTW, I do not believe the R2 >0.6 in Figure 9 given such a wide range of scattering points.
Third, the future projections were doubtful. This study used daily meteorology from four RCP scenarios output by MirocAR5. How to reduce the uncertainties from a single climate model projection? Normally, these scenarios showed very different tendencies of warming, indicating different fire probability. However, the projections of fire activities showed very similar results among these scenarios (e.g., Figure 4, Figure S5, Figure 12 …). Does it mean that the future wildfire activity in Siberia will increase at the similar rate no matter how warm the climate becomes? Furthermore, the updated fire module showed good correlations between burned fraction and burned biomass (Figure S11d), suggesting tight connections between these two parameters. The burned fraction is projected to increase continuously after the year 2040 (Figure S5b). Then why burned biomass showed such a large fluctuation over the same period?
Finally, the quality of result presentations was low. For example, the figures were very similar among the subplots of Figure 4e-4h, Figure, Figure S4e-S4h, Figure S6. Such information is useless as the readers could not tell their differences. Figure S28 showed the projected fire emissions of 33 trace gases by putting all the subplots together without any summary. It’s difficult to tell their differences and the main conclusions. The authors also spent great efforts in comparing results from the default and updated fire modules (e.g., Figures S4, S5, S9, S10, S11). While it’s important to understand how different the fire predictions before and after the improvement of fire modules, the authors could show some key results (e.g., Figure S11) and put more efforts in the validations of the updated model against observations, not only for fire activities but also some ecosystem parameters (e.g., tree height, biomass, NPP).
Figure 10: What’s the meaning of the shadings in (a)? What’s the meaning of the points in (b)?
Figure S5: What's the reason for the sharp decline of burned fraction around 2035 in (b)?

Citation: https://doi.org/10.5194/egusphere-2024-105-RC3
- AC3: 'Reply on RC3', Tomomichi Kato, 06 May 2024
  
  Thank you for taking the time to review our manuscript. We appreciate your acknowledgment that the topic of our study aligns well with the scope of Biogeosciences (BG). Your feedback regarding the presentation and clarity of our results is noted, and we apologize for any confusion or inconsistencies encountered.
  
  We understand the importance of ensuring the credibility of our model and projections. Your comments have raised valid concerns in this regard, and we are committed to addressing them through a major revision of the manuscript. We will carefully reevaluate our methodology, results, and interpretations to ensure accuracy and coherence throughout the manuscript. Moreover, we will provide additional clarification and justification for our findings to mitigate any perceived contradictions. We recognize the significance of transparently communicating our research outcomes to facilitate a better understanding among readers.
  Your feedback is invaluable to us, and we are grateful for the opportunity to improve our manuscript with your guidance. We assure you of our willingness to make necessary adjustments to enhance the credibility and quality of our work. Thank you once again for your thorough review and constructive comments.
  
  Citation: https://doi.org/10.5194/egusphere-2024-105-AC3

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2024-105', Anonymous Referee #1, 16 Feb 2024
Reza et al. improved the fire module in the SEIB-DGVM model and used the model to project Siberia wildfire dynamics, and emissions under various climate scenarios. I appreciate the valuable model development and analysis, and it provide us important implications on how future climate change affects Siberia boreal forest. The paper is informative and has a lot of important results to discuss, but it is not well-written in terms of smoothness and readability. There are confusions on methodology and data usage. Most importantly, this manuscript over-emphasizes future RCP projections. However, historical validation is not clear and convincing. A more reasonable approach is to first thoroughly demonstrate the model performance during the historical period by comparing it with observations. Then discuss future projections and caveats. Below are my major comments.
There is no quantitative evidence of the model's performance. During the historical period, how did the model simulate burned area, and fire emissions, compared with existing datasets (e.g., GFED) at gridcell level? Plot scatter plots of modeled versus observed variables (burned area, emissions, biomass) will be super helpful. Showing R2 and RMSE of gridcell level comparison in the abstract is highly encouraged. As it is stated in the abstract the motivation for integrating SPITFIRE into DGVM is to improve the accuracy of fire modeling. Therefore, it is also important to show how much improvement has been achieved by SPITFIRE compared with DGVM’s default fire model.

Improvement from annual time step estimate to monthly is an important change. However, most of the fires last less than one month. How does the monthly time step fire module resolve a process that lasts much shorter than a month?

Human ignition is considered in E term (equation 2), how about human suppression on fire spread?

Methodology, between 2.2 model description and 2.3 model application, there seems missing a section about model calibration. How was the SPITFIRE module calibrated for burned areas or emissions? How was the DGVM model calibrated to capture observed AGB?

For future projection, are population density changes and lightning flash changes considered under future scenarios?

Section 2.4, the model is validated by GFED4 burned area and GFED4s burned fraction. However, GFED4s and GFED4 are different because GFED4S include smaller fires. How to reconcile GFED4s and GFED4 during validation?

Section 3.1-3.4. Historical validation is an important component of this study. I would like to suggest before discussing RCP results thoroughly compare the simulated burned area, emission, and biomass with observations. Discuss model performance and biases at gridcell level, and the regional level.

Section 3.5 Fire-off simulation is not mentioned in the methodology section. When was the fire turned off? Does the model run an extended spinup with fire-off? One would expect much larger aboveground biomass when the fire is turned off.

Figure 7 showing spatial coverage is confusing because GFED4s provide burned fraction, which should be directly compared with SEIB-DGVM at gridcell level. Figure 8, a and b are duplicated, both showed long-term monthly average dry matter emission. Figure 9, why dry matter emission is perfectly simulated by SEIB-DGVM (shown in Figure 8), but the performance of CO2 emission is much worse. It seems inconsistent. What does it look like, if plot a spatial average comparison of DM emissions between SEIB-DGVM with GFED4s?
Citation: https://doi.org/10.5194/egusphere-2024-105-RC1
- AC1: 'Reply on RC1', Tomomichi Kato, 06 May 2024
  
  Thank you for your valuable feedback on our manuscript. We appreciate your understanding of importance in Siberia's boreal forests under future climate scenarios. We understand your concerns regarding the clarity and readability of the manuscript, as well as the need for a stronger emphasis on historical validation. We will revise the manuscript to improve its smoothness and address any confusions regarding methodology and data usage. Additionally, we will prioritize demonstrating the model's performance during the historical period before discussing future projections. Once again, thank you for your constructive comments and we have replied to your comments and also adjusted our manuscript accordingly.
  
  Citation: https://doi.org/10.5194/egusphere-2024-105-AC1
RC2:
'Comment on egusphere-2024-105', Anonymous Referee #2, 16 Feb 2024

Please find my review attached.

Citation: https://doi.org/10.5194/egusphere-2024-105-RC2
- AC2: 'Reply on RC2', Tomomichi Kato, 06 May 2024
  
  Thank you for your thorough review of our manuscript, and we appreciate your constructive feedback and insightful suggestions for improvement.
  
  We understand your suggestion regarding the restructuring of the manuscript to enhance its clarity and focus on key aspects. We will carefully reassess the content of the results section to ensure that it effectively communicates the significance of our findings and their implications. Additionally, we acknowledge the importance of prioritizing the evaluation of the model's performance during the historical period before discussing future projections. We will revise the manuscript accordingly to improve its coherence and readability.
  Furthermore, we appreciate your recommendation to include an overview figure or table that systematically compares and summarizes the model performance for the old and new versions. This will indeed provide a clearer demonstration of the improvements made in the new version of the model. Regarding the outdated references, we will thoroughly review the literature and update the references with more recent ones to ensure the accuracy and relevance of our citations.
  
  We are grateful for your suggestion consideration of our manuscript for publication in Biogeosciences after major revision. Your feedback will undoubtedly contribute to enhancing the quality of our work, and we are committed to addressing all your suggestions in our revised manuscript.
  
  Once again, thank you very much for your time and valuable input to our manuscript.
  
  Citation: https://doi.org/10.5194/egusphere-2024-105-AC2
RC3:
'Comment on egusphere-2024-105', Anonymous Referee #3, 17 Feb 2024

This study projected future wildfire activities in Siberia by implementing a process-based fire module into a dynamic vegetation model. The main conclusion was that fire emissions continued to increase due to climate change. The authors also quantified the negative impacts of fire activity on tree mortality and NPP. While the topic of the study well fit the scope of BG, I found the presentation was very poor and many results were contradicting. I suggested a major revision but with strong doubts about the credibility of the model and the projections.
First, the model descriptions were unclear. The authors put most of details, including equations and descriptions, to the supplementary material. However, the key processes should be listed in the main text for clarity. Most important, the links between the fire module and vegetation model should be explicitly explained. For example, what parameters did the SEIB-DGVM provide for SPITFIRE, and how the fire activity affect the vegetation distribution and ecosystem productivity. Such information determines which variables to be validated against observations.
Second, the model validations were questionable. Normally, the validations come before the projections. However, this study put the validations of fire models in the section 3.7, far behind the future projections. Some of the validations were not necessary. For example, Figure S2 compared the input and output of lightning flash rate and population density, which were actually the input of fire models instead of the prediction. Some validations were not consistent between different presentations. For example, Figure S17a compared the simulated and observed dry matter. The model predictions showed poor performance by missing almost all the observed fire episodes. However, in Figure 8, the monthly validations of simulated dry matter showed perfect performance with almost the same magnitude (Figure 8a) and R2=1 (Figure 8b) against observations. These results were too good to be true and seemed not consistent with Figure S17. Furthermore, the perfect simulation of dry matter (Figure 8b) resulted in a poor prediction of CO2 emissions (Figure 9). Was such bias attributed only to the emission factors? Then why making such a great effort to develop the sophisticated fire module when the simplest parameters caused the largest biases? BTW, I do not believe the R2 >0.6 in Figure 9 given such a wide range of scattering points.
Third, the future projections were doubtful. This study used daily meteorology from four RCP scenarios output by MirocAR5. How to reduce the uncertainties from a single climate model projection? Normally, these scenarios showed very different tendencies of warming, indicating different fire probability. However, the projections of fire activities showed very similar results among these scenarios (e.g., Figure 4, Figure S5, Figure 12 …). Does it mean that the future wildfire activity in Siberia will increase at the similar rate no matter how warm the climate becomes? Furthermore, the updated fire module showed good correlations between burned fraction and burned biomass (Figure S11d), suggesting tight connections between these two parameters. The burned fraction is projected to increase continuously after the year 2040 (Figure S5b). Then why burned biomass showed such a large fluctuation over the same period?
Finally, the quality of result presentations was low. For example, the figures were very similar among the subplots of Figure 4e-4h, Figure, Figure S4e-S4h, Figure S6. Such information is useless as the readers could not tell their differences. Figure S28 showed the projected fire emissions of 33 trace gases by putting all the subplots together without any summary. It’s difficult to tell their differences and the main conclusions. The authors also spent great efforts in comparing results from the default and updated fire modules (e.g., Figures S4, S5, S9, S10, S11). While it’s important to understand how different the fire predictions before and after the improvement of fire modules, the authors could show some key results (e.g., Figure S11) and put more efforts in the validations of the updated model against observations, not only for fire activities but also some ecosystem parameters (e.g., tree height, biomass, NPP).
Figure 10: What’s the meaning of the shadings in (a)? What’s the meaning of the points in (b)?
Figure S5: What's the reason for the sharp decline of burned fraction around 2035 in (b)?

Citation: https://doi.org/10.5194/egusphere-2024-105-RC3
- AC3: 'Reply on RC3', Tomomichi Kato, 06 May 2024
  
  Thank you for taking the time to review our manuscript. We appreciate your acknowledgment that the topic of our study aligns well with the scope of Biogeosciences (BG). Your feedback regarding the presentation and clarity of our results is noted, and we apologize for any confusion or inconsistencies encountered.
  
  We understand the importance of ensuring the credibility of our model and projections. Your comments have raised valid concerns in this regard, and we are committed to addressing them through a major revision of the manuscript. We will carefully reevaluate our methodology, results, and interpretations to ensure accuracy and coherence throughout the manuscript. Moreover, we will provide additional clarification and justification for our findings to mitigate any perceived contradictions. We recognize the significance of transparently communicating our research outcomes to facilitate a better understanding among readers.
  Your feedback is invaluable to us, and we are grateful for the opportunity to improve our manuscript with your guidance. We assure you of our willingness to make necessary adjustments to enhance the credibility and quality of our work. Thank you once again for your thorough review and constructive comments.
  
  Citation: https://doi.org/10.5194/egusphere-2024-105-AC3

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

ED: Reconsider after major revisions (06 May 2024) by David McLagan

AR by Tomomichi Kato on behalf of the Authors (15 May 2024) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (21 May 2024) by David McLagan

RR by Anonymous Referee #1 (27 May 2024)

RR by Anonymous Referee #3 (02 Jun 2024)

Suggestions for revision or reasons for rejection

The authors have made large efforts to improve the study following most of comments from reviewers. However, some concerns remain unsolved and should be responded carefully to improve the credibility of the future projection.

First, the validation is still confusing. Fig. 2 compares the spatial distribution of burned fraction from GFED4 and the model. However, it is only the agreement of coverage instead of the locations of hotspots. As I know, most of fires are located in the eastern Siberia at a narrow band. Please show the map of GFED4, the model, and their difference separately with values indicating the spatial distribution of burned fraction. Fig. 6 (original Fig. 9) compares the monthly fire emissions from model and GFED4. The authors provide the data link for the check of high R2 as I questioned. However, there are only 141 data samples for the period of 1997-2016 (20 years or 240 months). How are these 141 samples composed of? Furthermore, the R=0.78 is mainly due to the 0 values for both observations and models. If these zero values are removed, the R will be only ~0.3, suggesting that the model actually has low prediction of fire magnitude when the fires occur. Fig. 7 compared the PM2.5 emissions from the model and CAMS for 2004-2021 (18 years). Why there are only 12 data points on the scatter plot?

Second, the future projection seems unreasonable. Although no one could accurately predict future changes of fires, there are still some principles we could follow based on historical variations of boreal fires. The study shows very limited differences of future fire emissions among the four climate scenarios (Fig. 13). In addition, under the RCP8.5 to RCP2.6 scenarios, overall emissions by 2100 are projected to increase by 2.6%, 1.9%, 1.05% and 1.04% compared to 2000 emissions (Figure S28). These changes are too small to believe as future climate is quite different among the four scenarios and the RCP8.5 scenario projects an extremely warming world. The authors claimed that “the fire calculation does not directly use/consider the temperature variable. Instead, the fire variable is estimated based on the calculation of a chain of variables ranging from fuel availability fuel load (litter + aboveground biomass), moisture content…” I think the moisture content and fuel load should also be different under these climate scenarios. How the authors consider the changes of these driving factors in the projection?

Hide

RR by Anonymous Referee #2 (04 Jun 2024)

ED: Reconsider after major revisions (04 Jun 2024) by David McLagan

AR by Tomomichi Kato on behalf of the Authors (17 Jul 2024) Author's response Author's tracked changes Manuscript

ED: Publish subject to technical corrections (29 Jul 2024) by David McLagan

After review of the authors' response to reviewer comments and the tracked version of the manuscript, I am now happy to confirm that the manuscript can be accepted for publication (after some minor technical corrections). I greatly appreciate how responsive and collegial the authors have been to the quality reviews that have been provided in improving the quality of the manuscript.
One remaining concern that should be addressed relates to reviewer 1's comments about health exposure levels of CO2 not being relevant to this work. This is a regional wildfire model and the CO2 exposures generated under these scenarios do not really present direct human health exposure concerns for the general public. There are obviously indirect human health concerns relating to climate change, but this is very different to direct, acute exposures from CO2. Hence, please remove the sentence: "Additionally, prolonged exposure to very high CO₂ concentrations at ground level has negative impacts on health (Jacobson et al., 2019)." from the manuscript due to lack of relevancy. Also, the previous sentence " This event also affect the agricultural sector positively and negatively depending on the region, environment, and crop types agriculture (Kimball and Idso, 1983)." lacks relevancy as well and should also be removed. Discussing the implications of climate change on the global agricultural sector is outside the scope of the study examining future scenarios of wildfire in Fenno-Scandinavia. After consideration of these minor changes and technical review by the editorial team, the study can be published. Congratulations to the authors and a huge thanks to the efforts of the reviewers.

Hide

AR by Tomomichi Kato on behalf of the Authors (31 Jul 2024) Manuscript

Journal article(s) based on this preprint

26 Sep 2024

Future projections of Siberian wildfire and aerosol emissions

Reza Kusuma Nurrohman, Tomomichi Kato, Hideki Ninomiya, Lea Végh, Nicolas Delbart, Tatsuya Miyauchi, Hisashi Sato, Tomohiro Shiraishi, and Ryuichi Hirata

Biogeosciences, 21, 4195–4227, https://doi.org/10.5194/bg-21-4195-2024,https://doi.org/10.5194/bg-21-4195-2024, 2024

Short summary

Reza Kusuma Nurrohman, Tomomichi Kato, Hideki Ninomiya, Lea Végh, Nicolas Delbart, Tatsuya Miyauchi, Hisashi Sato, Tomohiro Shiraishi, and Ryuichi Hirata

Supplement

https://doi.org/10.5194/egusphere-2024-105-supplement

Model code and software

SEIB-DGVM with SPITFIRE Code Reza Kusuma Nurrohman https://doi.org/10.5281/zenodo.8299732

Reza Kusuma Nurrohman, Tomomichi Kato, Hideki Ninomiya, Lea Végh, Nicolas Delbart, Tatsuya Miyauchi, Hisashi Sato, Tomohiro Shiraishi, and Ryuichi Hirata

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Short summary

SPITFIRE fire module was integrated into SEIB Dynamic Global Vegetation Model to improve the model's accuracy in depicting forest fire frequency, intensity, and extent in Siberia. Projected fires showed a continuous increase in higher emissions of greenhouse gases and aerosols from 2023 to 2100 under all RCP scenarios. This study contributes to a better understanding of fire dynamics, land ecosystem-climate interactions, and global material cycles under the threat of escalating fires in Siberia.


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